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  • Here we present the GIS dataset for the surficial geology map for the Vestfold Hills, East Antarctica. On the coast of Prydz Bay, the region is one of the largest ice-free areas in Antarctica. Surficial geology mapping at 1:2000 was undertaken with field observations in the 2018/19 and 2019/20 summer seasons as well as aerial photography and satellite imagery interpretation. Units are based on the Geological Survey of Canada Surficial Data Model Version 2.4.0 (Deblonde et al 2019). This geodatabase, set of layer files (including sample and field observation sites), and metadata statement complement the flat pdf map published in 2021 - http://pid.geoscience.gov.au/dataset/ga/145535.

  • Background Land cover is the observed physical cover on the Earth's surface including trees, shrubs, grasses, soils, exposed rocks, water bodies, plantations, crops and built structures. A consistent, Australia-wide land cover product helps understanding of how the different parts of the environment change and inter-relate. Earth observation data recorded over a period of time firstly allows the observation of the state of land cover at a specific time and secondly the way that land cover changes by comparison between times. What this product offers DEA Land Cover provides annual land cover classifications for Australia using the Food and Agriculture Organisation Land Cover Classification System taxonomy Version 2 (Di Gregorio and Jansen, 1998; 2005). DEA Land Cover divides the landscape into six base land cover types, which are then further detailed in sub-classes. The structure of base and sub-classes is as follows: - Cultivated Terrestrial Vegetation - percentage of cover - life form (herbaceous only) - Natural Terrestrial Vegetation - percentage of cover - life form (herbaceous only) - Natural Aquatic Vegetation - percentage of cover - life form (herbaceous only) - water seasonality - Artificial Surfaces - Natural Bare - percentage of bare - Aquatic - water persistence - intertidal area

  • The Science Strategy 2028 Implementation Plan outlines the activities and actions across Geoscience Australia, led by the Chief Scientist, to operationalise the 'key implementations' of Science Strategy 2028. The discrete activities articulated in this Implementation Plan stem from mapping the six Science Principles against Geoscience Australia's Strategy 2028 Core Commitments. The Implementation Plan outlines the context and strategic rationale for a range of Geoscience Australia's activities led through the Office of the Chief Scientist, including the Science Evaluations, scientific capability and capacity mapping, and geoscientific engagement with First Nations communities and perspectives.

  • Geoscience Australia's value to the nation, outlined in its Strategy 2028 decadal plan, is through its science. However, the way that the organisation applies its science and achieves impact cannot be taken for granted. Science Strategy 2028, launched in late-2021, presents a guiding strategic framework for delivering the science that underpins our core business. This Science Strategy outlines the organisation's longstanding Science Principles as a business imperative and as key tenets for maintaining Geoscience Australia's standing as the nation’s trusted advisor on Australia’s geoscience and geography. Through the Science Strategy, Geoscience Australia will continue to integrate and achieve the six Science Principles through its business: to deliver relevant, collaborative, quality, transparent and communicated science, with a view to sustain our scientific capability.

  • Abstract: Tsunami inundation is rare on most coastlines, but large events can have devasting consequences for life and infrastructure. There is demand for inundation hazard maps to guide risk-management actions, such as the design of tsunami evacuation zones, tsunami-resilient infrastructure, and insurance. But the frequency of tsunami-generating processes (e.g., large earthquakes, landslides, and volcanic collapses) is usually very uncertain. This reflects limitations in scientific knowledge, and the short duration of historical records compared to the long inter-event times of dangerous tsunamis. Consequently, tsunami hazards are subject to large uncertainties which should be clearly communicated to inform risk-management decisions. Probabilistic Tsunami Hazard Assessment (PTHA) offers a structured approach to quantifying tsunami hazards and the associated uncertainties, while integrating data, models, and expert opinion. For earthquake-generated tsunamis, several national and global-scale PTHAs provide databases of hypothetical scenarios, scenario occurrence-rates and their uncertainties. Because these “offshore PTHAs” represent the coast at coarse spatial resolutions (~ 1-2 km) they are not directly suitable for onshore risk management and can only simulate tsunami waveforms accurately in deep-water, far from the coast. Yet because offshore PTHAs can use earthquake and tsunami data at global scales, they offer relatively well tested representations of earthquake-tsunami sources, occurrence-rates, and uncertainties. Furthermore, by combining an offshore PTHA with a high-resolution coastal inundation model, the resulting onshore tsunami hazard can in-principle be derived at spatial resolutions appropriate for risk management (~ 10 m) for any site of interest. This study considers the computational problem of rigorously transforming offshore PTHAs into site-specific onshore PTHAs. In theory this can be done by using a high-resolution hydrodynamic model to simulate inundation for every scenario in the offshore PTHA. In practice this is computationally prohibitive, because modern offshore PTHAs contain too many scenarios (on the order of 1 million) and inundation models are computationally demanding. Monte-Carlo sampling offers a rigorous alternative that requires less computation, because inundation simulations are only required for a random subset of scenarios. It is also known to converge to the correct solution as the number of scenarios is increased. This study develops several approaches to reduce Monte-Carlo errors at the onshore site of interest, for a given computational cost. As compared to existing Monte-Carlo approaches for offshore-to-onshore PTHA, the key novel idea is to use deep-water tsunami wave heights (modelled by the offshore PTHA) to estimate the relative “importance” of each scenario near the onshore site of interest, prior to inundation simulation. Scenarios are randomly sampled from the offshore PTHA in a way that over-represents the “important” scenarios, and the theory of importance sampling enables weighting these scenarios so as to correct for the sampling bias. This can greatly reduce Monte-Carlo errors for a given sampling effort. In addition, because importance-sampling is analytically tractable, the variance of the Monte-Carlo errors can be estimated at offshore sites prior to sampling. This helps modellers to estimate the adequacy of a proposed Monte-Carlo sampling scheme prior to expensive inundation computation. The analytical variance result also enables the theory of optimal-sampling to be applied in a way that to reduces the Monte-Carlo variance, by non-uniformly sampling from earthquakes of different magnitudes. The new techniques are applied to an onshore earthquake-tsunami PTHA in Tongatapu, the main island of Tonga. In combination the new techniques lead to efficiency improvements equivalent to simulating 4-18 times more scenarios, as compared with commonly used Monte-Carlo methods for onshore PTHA. They also enable the hazard uncertainties in the offshore PTHA to be translated onshore, where they are of most significance to risk management decision-making. The greatest accuracy improvements occur for large tsunamis, and for computations that represent uncertainties in the hazard.

  • NDI Carrara 1 is a deep stratigraphic drill hole (~1751m) completed in 2020 as part of the MinEx CRC National Drilling Initiative (NDI) in collaboration with Geoscience Australia and the Northern Territory Geological Survey. It is the first test of the Carrara Sub-basin, a depocentre newly discovered in the South Nicholson region based on interpretation from seismic surveys (L210 in 2017 and L212 in 2019) recently acquired as part of the Exploring for the Future program. The drill hole intersected approximately 1100 m of Proterozoic sedimentary rocks uncomformably overlain by 630 m of Cambrian Georgina Basin carbonates. This report presents the Pb isotopes analyses conducted on 22 selected whole rock samples of NDI Carrara 1 undertaken by University of Melbourne.

  • Background: This is a sub-product of eoscience Australia Sentinel-2B MSI Analysis Ready Data Collection 3 - DEA Surface Reflectance (Sentinel-2B MSI). See the parent product for more information. The contextual information related to a dataset is just as valuable as the data itself. This information, also known as data provenance or data lineage, includes details such as the data’s origins, derivations, methodology and processes. It allows the data to be replicated and increases the reliability of derivative applications. Data that is well-labelled and rich in spectral, spatial and temporal attribution can allow users to investigate patterns through space and time. Users are able to gain a deeper understanding of the data environment, which could potentially pave the way for future forecasting and early warning systems. The surface reflectance data produced by NBART requires accurate and reliable data provenance. Attribution labels, such as the location of cloud and cloud shadow pixels, can be used to mask out these particular features from the surface reflectance analysis, or used as training data for machine learning algorithms. Additionally, the capacity to automatically exclude or include pre-identified pixels could assist with emerging multi-temporal and machine learning analysis techniques. What this product offers: This product contains a range of pixel-level observation attributes (OA) derived from satellite observation, providing rich data provenance: - null pixels - clear pixels - cloud pixels - cloud shadow pixels - snow pixels - water pixels - spectrally contiguous pixels - terrain shaded pixels It also features the following pixel-level information pertaining to satellite, solar and sensing geometries: - solar zenith - solar azimuth - satellite view - incident angle - exiting angle - azimuthal incident - azimuthal exiting - relative azimuth - timedelta

  • Background: This is a sub-product of DEA Surface Reflectance (Sentinel-2B MSI). See the parent product for more information. Reflectance data at top of atmosphere (TOA) collected by Sentinel-2B MSI sensors can be affected by atmospheric conditions, sun position, sensor view angle, surface slope and surface aspect. Surfaces with varying terrain can introduce inconsistencies to optical satellite images through irradiance and bidirectional reflectance distribution function (BRDF) effects. For example, slopes facing the sun appear brighter compared with those facing away from the sun. Likewise, many surfaces on Earth are anisotropic in nature, so the signal picked up by a satellite sensor may differ depending on the sensor’s position. These need to be reduced or removed to ensure the data is consistent and can be compared over time. What this product offers: This product takes Sentinel-2B MSI imagery captured over the Australian continent and corrects the inconsistencies across the land and coastal fringe. It achieves this using Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance (NBAR). In addition, this product has a terrain illumination correction applied to correct for varying terrain. The resolution is a 10/20/60 m grid based on the ESA level 1C archive. Applications: - The development of derivative products to monitor land, inland waterways and coastal features, such as: - urban growth - coastal habitats - mining activities - agricultural activity (e.g. pastoral, irrigated cropping, rain-fed cropping) - water extent - The development of refined information products, such as: - areal units of detected surface water - areal units of deforestation - yield predictions of agricultural parcels - Compliance surveys - Emergency management

  • Map of Petroleum and Greenhouse Gas Storage Titles of Australia 2022 as at March 2022

  • Background These are the statistics generated from the DEA Water Observations (Water Observations from Space) suite of products, which gives summaries of how often surface water was observed by the Landsat satellites for various periods (per year, per season and for the period from 1986 to the present). Water Observations Statistics (WO-STATS) provides information on how many times the Landsat satellites were able to clearly see an area, how many times those observations were wet, and what that means for the percentage of time that water was observed in the landscape. What this product offers Each dataset in this product consists of the following datasets: - Clear Count: how many times an area could be clearly seen (i.e. not affected by clouds, shadows or other satellite observation problems) - Wet Count: how many times water was detected inobservations that were clear - Water Summary: what percentage of clear observations were detected as wet (i.e. the ratio of wet to clear as a percentage) As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the input water classifications, and can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations Filtered Statistics (WO-FILT-STATS) product, which provides a noise-reduced view of the all-of-time water summary. WO-STATS is available in multiple forms, depending on the length of time over which the statistics are calculated. At present the following are available: WO-STATS:statistics calculated from the full depth of time series (1986 to present) WO-STATS-ANNUAL:statistics calculated from each calendar year (1986 to present) WO-STATS-NOV-MAR:statistics calculated yearly from November to March (1986 to present) WO-STATS-APR-OCT:statistics calculated yearly from April to October (1986 to present)